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Multi-terminal DC (MTDC) networks interconnect isolated systems, asynchronous areas, and renewable energy resources. However, ensuring stability and proper dynamic behavior of MTDC networks can be challenging. Power electronic converters offer increased control capabilities to achieve predictable and stable dynamics in the face of disturbances, faults, and sustained oscillations. The DC-side power system stabilizer (DC-PSS) is a supplementary controller that relies on optimal placement for maximized effectiveness. Observers can estimate the state of relevant system variables for the feedback controller without system-wide communication. This paper proposes an optimally-placed observer-based augmented DC-PSS for enhanced voltage stability in MTDC networks. The design procedure assumes no knowledge of the structure and parameters of the inner controllers of the terminal converters. We present a mathematical model and discuss the observer and control design procedures, and simulation results show the benefits and potential drawbacks of the proposed approach. Our observer-based DC-PSS relies on system-wide DC-bus voltage estimates from a single measurement, making it less vulnerable to communication delays and cyberattacks. Our work contributes to the field of supplementary controllers in MTDC networks and provides insights into future research directions toward stable and reliable DC power systems., QC 20231023

The increasing integration of variable renewable energy resources through power electronics has brought about substantial changes in the structure and dynamics of modern power systems. In response to these transformations, there has been a surge in the development of tools and algorithms leveraging real-time computational power to enhance system operation and stability. Data-driven methods have emerged as practical approaches for extracting reliable representations from non-linear system data, enabling the identification of dynamics and system parameters essential for analysing stability and ensuring reliable operation. This study provides a comprehensive review of recent contributions in the literature concerning the application of data-driven identification, analysis, and control methods in various aspects of power system operation. Specifically, the focus is on frequency support, power oscillation detection, and damping, which play crucial roles in maintaining grid stability. By discussing the challenges posed by parametric uncertainties, load and source variability, and reduced system inertia, this review sheds light on the opportunities for future research endeavours., QC 20231030

Phasor measurement units (PMUs) provide accurate and high-fidelity measurements in order to monitor the state of the power grid and support various control and planning tasks. However, PMUs have a high installation cost prohibiting their massive deployment. Minimizing the number of installed PMUs needs to be achieved while also maintaining full observability of the network. At the same time, data integrity attacks on PMU measurements can cause mislead power system control and operation routines. In this paper, a bi-level stochastic non-cooperative game-based placement model is proposed for PMU allocation in the presence of cyber-attack risks. In the first level, the protection of individual PMU placed in a network is addressed, while considering the interaction between the grid operator and the attacker with respective resource constraints. In the second level, the attacker observes the placement of the PMUs and compromises them, with the aim of maximizing the state estimation error and reducing the observability of the network. The proposed technique is deployed in the IEEE-9 bus test system. The results demonstrate a 9% reduction in the cost incurred by the power grid operator for deploying PMUs while considering cyber-risks., Comment: 2023 IEEE Belgrade PowerTech

Traditionally, Volt/VAR optimization (VVO) is performed in distribution networks through legacy devices such as on-load tap changers (OLTCs), voltage regulators (VRs), and capacitor banks. With the amendment in IEEE 1547 standard, distributed energy resources (DERs) can now provide reactive power support to the grid. For this, renewable energy-based DERs, such as PV, are interfaced with the distribution networks through smart inverters (SIs). Due to the intermittent nature of such resources, VVO transforms into a dynamic problem that requires extensive communication between the VVO controller and devices performing the VVO scheme. This communication, however, can be potentially tampered with by an adversary rendering the VVO ineffective. In this regard, it is important to assess the impact of cyberattacks on the VVO scheme. This paper develops a real-time co-simulation setup to assess the effect of cyberattacks on VVO. The setup consists of a real-time power system simulator, a communication network emulator, and a master controller in a system-in-the-loop (SITL) setup. The DNP3 communication protocol is adopted for the underlying communication infrastructure. The results show that corrupted communication messages can lead to violation of voltage limits, increased number of setpoint updates of VRs, and economic loss., Comment: 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

The management of distributed and heterogeneous modern power networks necessitates the deployment of communication links, often characterized by limited bandwidth. This paper presents an event detection mechanism that significantly reduces the volume of data transmission to perform necessary control actions, using a scalable scheme that enhances the stability and reliability of power grids. The approach relies on implementing a linear quadratic regulator and the execution of a pair of Luenberger observers. The linear quadratic regulator minimizes the amount of energy required to achieve the control actions. Meanwhile, the Luenberger observers estimate the unmeasured states from the sensed states, providing the necessary information to trigger the event detection mechanism. The effectiveness of the method is tested via time-domain simulations on the IEEE 13-node test feeder interfaced with inverter-based distributed generation systems and the proposed observed-based event-triggered controller. The results demonstrate that the presented control scheme guarantees the bounding of the system states to a pre-specified limit while reducing the number of data packet transmissions by 39.8%., Comment: 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

The evolving High Voltage Direct Current (HVDC) technology integrated into the modern power grids can help improve operation stability and damp undesired low-frequency oscillations in the system. This paper presents a Power Oscillation Damping (POD) strategy for power systems with a hybrid (LCC-VSC) HVDC link. The work consists of a centralized supervision algorithm that monitors the dynamics of several system variables and sets the appropriate gains to the POD controller from a lookup table (LUT) generated offline via simulation-based Particle Swarm optimization analysis. The mathematical modeling for the test system with an embedded HVDC link is presented, and the optimal tuning problem is defined using performance-oriented objective functions. Details for the detection and scheduling algorithm, LUT construction, and controller structure are provided. The nonlinear simulation model is implemented in MATLAB, and the results support the effectiveness of the proposed approach., QC 20221003

As the world’s largest annually harvested crop, optimization of maize (Zea mays) protective biochemical defenses that impede pest and pathogen attack will be essential to mitigate significant yield losses that could compromise our global food security. To better understand the diversity of pathogen elicited maize antibiotics, we leveraged a rapid sample preparation technique termed Vapour Phase Extraction (VPE) coupled with mass spectrometry (MS), high-performance liquid chromatography (HPLC) and Nuclear Magnetic Resonance (NMR) spectroscopy. We focused experiments on roots of field-grown maize lines discovered to produce high levels of terpenoid defenses. Leveraging the power of VPE, HPLC, and NMR, we found a series of novel modified dolabradiene derivatives, predictably part of the dolabralexin diterpenoid family of maize defenses. To generate endogenous evidence that the novel dolabralexins are products of diterpene synthases Anther ear 2 (ZmAn2) and Kaurene Synthase-Like 4 (ZmKSL4), we analyzed Zman2 and Zmksl4 mutants and demonstrated an absence of all dolabralexin-related metabolites. Chemical analyses of maize Biparental Recombinant Inbred Line (RIL) mapping population and an association panel coupled with global gene co-expression analyses using Mutual Ranks and Agrobacterium-mediated transient overexpression enzyme assays in Nicotiana benthamiana using different combinations of ZmAn2 and ZmKSL4 with cytochrome (CYP) P450 monooxygenases have revealed that ZmCYP71Z16 may contribute to the production of complex dolabralexin products.

As the world’s largest annually harvested crop, optimization of maize (Zea mays) protective biochemical defenses that impede pest and pathogen attack will be essential to mitigate significant yield losses that could compromise our global food security. To better understand the diversity of pathogen elicited maize antibiotics, we leveraged a rapid sample preparation technique termed Vapour Phase Extraction (VPE) coupled with mass spectrometry (MS), high-performance liquid chromatography (HPLC) and Nuclear Magnetic Resonance (NMR) spectroscopy. We focused experiments on roots of field-grown maize lines discovered to produce high levels of terpenoid defenses. Leveraging the power of VPE, HPLC, and NMR, we found a series of novel modified dolabradiene derivatives, predictably part of the dolabralexin diterpenoid family of maize defenses. To generate endogenous evidence that the novel dolabralexins are products of diterpene synthases Anther ear 2 (ZmAn2) and Kaurene Synthase-Like 4 (ZmKSL4), we analyzed Zman2 and Zmksl4 mutants and demonstrated an absence of all dolabralexin-related metabolites. Chemical analyses of maize Biparental Recombinant Inbred Line (RIL) mapping population and an association panel coupled with global gene co-expression analyses using Mutual Ranks and Agrobacterium-mediated transient overexpression enzyme assays in Nicotiana benthamiana using different combinations of ZmAn2 and ZmKSL4 with cytochrome (CYP) P450 monooxygenases have revealed that ZmCYP71Z16 may contribute to the production of complex dolabralexin products.

Human genome sequencing efforts have greatly expanded, and a plethora of missense variants identified both in patients and in the general population is now publicly accessible. Interpretation of the molecular-level effect of missense variants, however, remains challenging and requires a particular investigation of amino acid substitutions in the context of protein structure and function. Answers to questions like 'Is a variant perturbing a site involved in key macromolecular interactions and/or cellular signaling?', or 'Is a variant changing an amino acid located at the protein core or part of a cluster of known pathogenic mutations in 3D?' are crucial. Motivated by these needs, we developed MISCAST (missense variant to protein structure analysis web suite; http://miscast.broadinstitute.org/). MISCAST is an interactive and user-friendly web server to visualize and analyze missense variants in protein sequence and structure space. Additionally, a comprehensive set of protein structural and functional features have been aggregated in MISCAST from multiple databases, and displayed on structures alongside the variants to provide users with the biological context of the variant location in an integrated platform. We further made the annotated data and protein structures readily downloadable from MISCAST to foster advanced offline analysis of missense variants by a wide biological community.

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variations on protein structure and function being especially challenging. Here we characterize the three-dimensional (3D) amino acid positions affected in pathogenic and population variants from 1,330 disease-associated genes using over 14,000 experimentally solved human protein structures. By measuring the statistical burden of variations (i.e., point mutations) from all genes on 40 3D protein features, accounting for the structural, chemical, and functional context of the variations' positions, we identify features that are generally associated with pathogenic and population missense variants. We then perform the same amino acid-level analysis individually for 24 protein functional classes, which reveals unique characteristics of the positions of the altered amino acids: We observe up to 46% divergence of the class-specific features from the general characteristics obtained by the analysis on all genes, which is consistent with the structural diversity of essential regions across different protein classes. We demonstrate that the function-specific 3D features of the variants match the readouts of mutagenesis experiments for BRCA1 and PTEN, and positively correlate with an independent set of clinically interpreted pathogenic and benign missense variants. Finally, we make our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step toward translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the variants' pathogenicity in terms of the perturbed molecular mechanisms.

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variations on protein structure and function being especially challenging. Here we characterize the three-dimensional (3D) amino acid positions affected in pathogenic and population variants from 1,330 disease-associated genes using over 14,000 experimentally solved human protein structures. By measuring the statistical burden of variations (i.e., point mutations) from all genes on 40 3D protein features, accounting for the structural, chemical, and functional context of the variations’ positions, we identify features that are generally associated with pathogenic and population missense variants. We then perform the same amino acid-level analysis individually for 24 protein functional classes, which reveals unique characteristics of the positions of the altered amino acids: We observe up to 46% divergence of the class-specific features from the general characteristics obtained by the analysis on all genes, which is consistent with the structural diversity of essential regions across different protein classes. We demonstrate that the function-specific 3D features of the variants match the readouts of mutagenesis experiments for BRCA1 and PTEN, and positively correlate with an independent set of clinically interpreted pathogenic and benign missense variants. Finally, we make our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step toward translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the variants’ pathogenicity in terms of the perturbed molecular mechanisms.

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variations on protein structure and function being especially challenging. Here we characterize the three-dimensional (3D) amino acid positions affected in pathogenic and population variants from 1,330 disease-associated genes using over 14,000 experimentally solved human protein structures. By measuring the statistical burden of variations (i.e., point mutations) from all genes on 40 3D protein features, accounting for the structural, chemical, and functional context of the variations’ positions, we identify features that are generally associated with pathogenic and population missense variants. We then perform the same amino acid-level analysis individually for 24 protein functional classes, which reveals unique characteristics of the positions of the altered amino acids: We observe up to 46% divergence of the class-specific features from the general characteristics obtained by the analysis on all genes, which is consistent with the structural diversity of essential regions across different protein classes. We demonstrate that the function-specific 3D features of the variants match the readouts of mutagenesis experiments for BRCA1 and PTEN, and positively correlate with an independent set of clinically interpreted pathogenic and benign missense variants. Finally, we make our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step toward translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the variants’ pathogenicity in terms of the perturbed molecular mechanisms.

Human genome sequencing efforts have greatly expanded, and a plethora of missense variants identified both in patients and in the general population is now publicly accessible. Interpretation of the molecular-level effect of missense variants, however, remains challenging and requires a particular investigation of amino acid substitutions in the context of protein structure and function. Answers to questions like 'Is a variant perturbing a site involved in key macromolecular interactions and/or cellular signaling?', or 'Is a variant changing an amino acid located at the protein core or part of a cluster of known pathogenic mutations in 3D?' are crucial. Motivated by these needs, we developed MISCAST (missense variant to protein structure analysis web suite; http://miscast.broadinstitute.org/). MISCAST is an interactive and user-friendly web server to visualize and analyze missense variants in protein sequence and structure space. Additionally, a comprehensive set of protein structural and functional features have been aggregated in MISCAST from multiple databases, and displayed on structures alongside the variants to provide users with the biological context of the variant location in an integrated platform. We further made the annotated data and protein structures readily downloadable from MISCAST to foster advanced offline analysis of missense variants by a wide biological community.

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variations on protein structure and function being especially challenging. Here we characterize the three-dimensional (3D) amino acid positions affected in pathogenic and population variants from 1,330 disease-associated genes using over 14,000 experimentally solved human protein structures. By measuring the statistical burden of variations (i.e., point mutations) from all genes on 40 3D protein features, accounting for the structural, chemical, and functional context of the variations' positions, we identify features that are generally associated with pathogenic and population missense variants. We then perform the same amino acid-level analysis individually for 24 protein functional classes, which reveals unique characteristics of the positions of the altered amino acids: We observe up to 46% divergence of the class-specific features from the general characteristics obtained by the analysis on all genes, which is consistent with the structural diversity of essential regions across different protein classes. We demonstrate that the function-specific 3D features of the variants match the readouts of mutagenesis experiments for BRCA1 and PTEN, and positively correlate with an independent set of clinically interpreted pathogenic and benign missense variants. Finally, we make our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step toward translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the variants' pathogenicity in terms of the perturbed molecular mechanisms.

Wrapping a computation domain with a perfectly matched layer (PML) is one of the most effective methods of imitating/approximating the radiation boundary condition in Maxwell and wave equation solvers. Many PML implementations often use a smoothly-increasing attenuation coefficient to increase the absorption for a given layer thickness, and, at the same time, to reduce the numerical reflection from the interface between the computation domain and the PML. In discontinuous Galerkin time-domain (DGTD) methods, using a PML coefficient that varies within a mesh element requires a different mass matrix to be stored for every element and therefore significantly increases the memory footprint. In this work, this bottleneck is addressed by applying a weight-adjusted approximation to these mass matrices. The resulting DGTD scheme has the same advantages as the scheme that stores individual mass matrices, namely higher accuracy (due to reduced numerical reflection) and increased meshing flexibility (since the PML does not have to be defined layer by layer) but it requires significantly less memory.

Human genome sequencing efforts have greatly expanded, and a plethora of missense variants identified both in patients and in the general population is now publicly accessible. Interpretation of the molecular-level effect of missense variants, however, remains challenging and requires a particular investigation of amino acid substitutions in the context of protein structure and function. Answers to questions like 'Is a variant perturbing a site involved in key macromolecular interactions and/or cellular signaling?', or 'Is a variant changing an amino acid located at the protein core or part of a cluster of known pathogenic mutations in 3D?' are crucial. Motivated by these needs, we developed MISCAST (missense variant to protein structure analysis web suite; http://miscast.broadinstitute.org/). MISCAST is an interactive and user-friendly web server to visualize and analyze missense variants in protein sequence and structure space. Additionally, a comprehensive set of protein structural and functional features have been aggregated in MISCAST from multiple databases, and displayed on structures alongside the variants to provide users with the biological context of the variant location in an integrated platform. We further made the annotated data and protein structures readily downloadable from MISCAST to foster advanced offline analysis of missense variants by a wide biological community.

Inference of the structural and functional consequences of amino acid-altering missense variants is challenging and not yet scalable. Clinical and research applications of the colossal number of identified missense variants is thus limited. Here we describe the aggregation and analysis of large-scale genomic variation and structural biology data for 1,330 disease-associated genes. Comparing the burden of 40 structural, physicochemical, and functional protein features of altered amino acids with 3-dimensional coordinates, we found 18 and 14 features that are associated with pathogenic and population missense variants, respectively. Separate analyses of variants from 24 protein functional classes revealed novel function-dependent vulnerable features. We then devised a quantitative spectrum, identifying variants with higher pathogenic variant-associated features. Finally, we developed a web resource (MISCAST; http://miscast.broadinstitute.org/) for interactive analysis of variants on linear and tertiary protein structures. The biological impact of missense variants available through the webtool will assist researchers in hypothesizing variant pathogenicity and disease trajectories.

Inference of the structural and functional consequences of amino acid-altering missense variants is challenging and not yet scalable. Clinical and research applications of the colossal number of identified missense variants is thus limited. Here we describe the aggregation and analysis of large-scale genomic variation and structural biology data for 1,330 disease-associated genes. Comparing the burden of 40 structural, physicochemical, and functional protein features of altered amino acids with 3-dimensional coordinates, we found 18 and 14 features that are associated with pathogenic and population missense variants, respectively. Separate analyses of variants from 24 protein functional classes revealed novel function-dependent vulnerable features. We then devised a quantitative spectrum, identifying variants with higher pathogenic variant-associated features. Finally, we developed a web resource (MISCAST; http://miscast.broadinstitute.org/) for interactive analysis of variants on linear and tertiary protein structures. The biological impact of missense variants available through the webtool will assist researchers in hypothesizing variant pathogenicity and disease trajectories.

Pulse forming networks and Marx generators are the classical rectangular waveform pulse generators (PGs). They are inflexible and their capacitors must be fully charged to the required voltage from 0V before delivering each high-voltage (HV) pulse. They are only able to generate unipolar pulses; if bipolar pulses are sought another generator fed from a negative supply voltage is added. Recently, several power electronics based PGs have been proposed. This paper presents an HV power electronics based PG, which is based on Half-Bridge Modular Multilevel Converter (HB-MMC) sub-modules (SMs) charged sequentially in a voltage boost mode. Each SM capacitor and main switch form a boost converter with the charging input supply and inductor. As a result, all SM capacitors are charged to a voltage greater than the input. During the discharging process the SM capacitors are connected in series, producing a rectangular HV pulse across the load. The proposed charging method allows a reduction in the converter footprint in comparison with recently proposed MMC sequentially charged PG topologies. Although only rectangular pulse waveforms are sought in this paper, a SM capacitor voltage balance method allows multilevel pulse generation. The viability of the proposed converter is confirmed by MATLAB/Simulink simulation and scaled-down experimentation.

Pulse forming networks and Marx generators are the classical rectangular waveform pulse generators (PGs). They are inflexible and their capacitors must be fully charged to the required voltage from 0V before delivering each high-voltage (HV) pulse. They are only able to generate unipolar pulses; if bipolar pulses are sought another generator fed from a negative supply voltage is added. Recently, several power electronics based PGs have been proposed. This paper presents an HV power electronics based PG, which is based on Half-Bridge Modular Multilevel Converter (HB-MMC) sub-modules (SMs) charged sequentially in a voltage boost mode. Each SM capacitor and main switch form a boost converter with the charging input supply and inductor. As a result, all SM capacitors are charged to a voltage greater than the input. During the discharging process the SM capacitors are connected in series, producing a rectangular HV pulse across the load. The proposed charging method allows a reduction in the converter footprint in comparison with recently proposed MMC sequentially charged PG topologies. Although only rectangular pulse waveforms are sought in this paper, a SM capacitor voltage balance method allows multilevel pulse generation. The viability of the proposed converter is confirmed by MATLAB/Simulink simulation and scaled-down experimentation.

Emotion recognition plays an important role in human-computer interaction (HCI) and has been extensively studied for decades. Although tremendous improvements have been achieved for posed expressions, recognizing human emotions in "close-to-real-world" environments remains a challenge. In this paper, we proposed two strategies to fuse information extracted from different modalities, i.e., audio and visual. Specifically, we utilized LBP-TOP, an ensemble of CNNs, and a bi-directional LSTM (BLSTM) to extract features from the visual channel, and the OpenSmile toolkit to extract features from the audio channel. Two kinds of fusion methods, i,e., feature-level fusion and model-level fusion, were developed to utilize the information extracted from the two channels. Experimental results on the EmotiW2018 AFEW dataset have shown that the proposed fusion methods outperform the baseline methods significantly and achieve better or at least comparable performance compared with the state-of-the-art methods, where the model-level fusion performs better when one of the channels totally fails.

A novel Identity-Free conditional Generative Adversarial Network (IF-GAN) was proposed for Facial Expression Recognition (FER) to explicitly reduce high inter-subject variations caused by identity-related facial attributes, e.g., age, race, and gender. As part of an end-to-end system, a cGAN was designed to transform a given input facial expression image to an "average" identity face with the same expression as the input. Then, identity-free FER is possible since the generated images have the same synthetic "average" identity and differ only in their displayed expressions. Experiments on four facial expression datasets, one with spontaneous expressions, show that IF-GAN outperforms the baseline CNN and achieves state-of-the-art performance for FER.

In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a novel PAT module with an associated PAT loss was proposed to learn features in a hierarchical tree structure organized according to attributes, where the final features are less affected by the attributes. Then, expression-related features are extracted from leaf nodes. Samples are probabilistically assigned to tree nodes at different levels such that expression-related features can be learned from all samples weighted by probabilities. We further proposed a semi-supervised strategy to learn the PAT-CNN from limited attribute-annotated samples to make the best use of available data. Experimental results on five facial expression datasets have demonstrated that the proposed PAT-CNN outperforms the baseline models by explicitly modeling attributes. More impressively, the PAT-CNN using a single model achieves the best performance for faces in the wild on the SFEW dataset, compared with the state-of-the-art methods using an ensemble of hundreds of CNNs., Comment: 10 pages

High-voltage (HV) pulses are used in electroporation to subject pulsed electric field (PEF) onto a sample under treatment. Pulse-waveform shape, voltage magnitude, pulse duration, and pulse repetition rate are the basic controllable variables required for particular PEF application. In practice, a custom-made pulse generator is dedicated for each PEF application with limited flexibility in changing these variables. In this paper, a universal pulse-waveform generator (UPG) is proposed, where the controller software algorithm can manipulate a basic generated multilevel pulse waveform to emulate many different PEF pulse waveforms. The commonly used PEF HV pulse waveforms can be generated as bipolar or monopolar with controllable pulse durations, repetition times, and voltage magnitudes. The UPG has the ability to generate multilevel pulses that have controllable dv/dt, which allows reduction of the electromagnetic interference generated by the converter. The UPG topology is based on half-bridge modular multilevel converter (HB-MMC) cells forming two transition arms in conjunction with two bistate arms, together creating an Hbridge. The HB-MMC cell capacitors provide a controllable energy source which charge from the dc input supply and discharge across the load, while the two bistate arms allow charging the HB-MMC cell capacitors. Hence, the UPG topology offers modularity, redundancy, and scalability. The HB-MMC individual cell capacitance is low and the cell voltages are balanced by employing the sorting and rotating algorithm used in conventional HB-MMC topologies for HV dc transmission applications. The viability of the proposed UPG converter is validated by MATLAB/Simulink simulation and scaled-down experimentation.

High-voltage (HV) pulses are used in pulsed electric field (PEF) applications to provide an effective electroporation process, a process in which harmful microorganisms are disinfected when subjected to a PEF. Depending on the PEF application, different HV pulse specifications are required such as the pulse-waveform shape, the voltage magnitude, the pulse duration, and the pulse repetition rate. In this paper, a generic pulsewaveform generator (GPG) is proposed, and the GPG topology is based on half-bridge modular multilevel converter (HB-MMC) cells. The GPG topology is formed of four identical arms of series-connected HB-MMC cells forming an H-bridge. Unlike the conventional HB-MMC-based converters in HVdc transmission, the GPG load power flow is not continuous which leads to smaller size cell capacitors utilization; hence, smaller footprint of the GPG is achieved. The GPG topology flexibility allows the controller software to generate a basic multilevel waveform which can be manipulated to generate the commonly used PEF pulse waveforms. Therefore, the proposed topology offers modularity, redundancy, and scalability. The viability of the proposed GPG converter is validated by MATLAB/Simulink simulation and experimentation.

Recognizing facial action units (AUs) from spontaneous facial expressions is still a challenging problem. Most recently, CNNs have shown promise on facial AU recognition. However, the learned CNNs are often overfitted and do not generalize well to unseen subjects due to limited AU-coded training images. We proposed a novel Incremental Boosting CNN (IB-CNN) to integrate boosting into the CNN via an incremental boosting layer that selects discriminative neurons from the lower layer and is incrementally updated on successive mini-batches. In addition, a novel loss function that accounts for errors from both the incremental boosted classifier and individual weak classifiers was proposed to fine-tune the IB-CNN. Experimental results on four benchmark AU databases have demonstrated that the IB-CNN yields significant improvement over the traditional CNN and the boosting CNN without incremental learning, as well as outperforming the state-of-the-art CNN-based methods in AU recognition. The improvement is more impressive for the AUs that have the lowest frequencies in the databases., Comment: NIPS2016

Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions. In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or the center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition., Comment: 8 pages, 3 figures

Differential-mode inverter topologies are promising for renewable energy generation since they offer advantages such as passive elements sizes reduction, having decreased power losses and reduced total cost. Single phase buck–boost differential-mode current-source inverters (DMCSIs) can deliver wider range of output voltage above or below the input dc voltage, which is necessary for high-efficiency modern renewable energy applications. Since these inverters have continuous input currents, they are suitable for maximum power point tracking (MPPT) operation of photovoltaic systems. Yet, the investigation, operation and implementation of these types of inverters have not been extensively discussed. As a drawback, the total dc side input current of a single-phase inverter consists of a dc component and an undesirable ac component. This ac component frequency is double the output voltage frequency and thus it affects MPPT resulting in reduced total efficiency. In this study, five promising DMCSIs are proposed and compared in terms of their total efficiencies, high-frequency ripple current in the dc side, total harmonic distortion, devices sizes and ratings. Moreover, the sliding mode controller's design and possible methods of eliminating the input second harmonic current are discussed. 2.5 kW bidirectional inverters are implemented to confirm the design flexibility of the proposed DMCSIs.

Differential-mode buck-boost inverters have merits such as reduced switch number, ability to provide voltages higher or lower than the input voltage magnitude, improved efficiency, reduced cost and size, and increased power density, especially in low-power applications. There are five buck-boost inverters that can provide flexible output voltage without the need of a large electrolytic input side capacitor, which degrades the reliability of inverters. The continuous input current of these inverters is appropriate for maximum power point tracking operation in photovoltaic and fuel cells applications. Three of the five inverters can be isolated with high-frequency-link transformers where the common-mode leakage current can be mitigated. However, the performance and control of such converters have not been discussed in detail. In this paper, the five possible single-stage three-phase differential-mode buck-boost inverters with continuous input current are investigated and compared in terms of total losses, maximum ripple current, total harmonic distortion, and device and passive element ratings. In addition, the possible methods are presented for eliminating the input third-order harmonic current, resulting from the stored energy in the passive elements, as well as the output second-order harmonic currents. The ability for isolating the input and output sides of the inverters with a small-high frequency transformers is discussed. A changeable-terminal 2.5-kW bidirectional inverter is used to validate the design flexibility of the inverter topologies, when digital signal processor-controlled.

The role of irreversible electroporation using pulsed electric field is to generate high-voltage (HV) pulses with a predefined magnitude and duration. These HV pulses are applied to the treatment chamber until decontamination of the sample is completed. In this paper, a new topology for HV rectangular pulse generation for water disinfection applications is introduced. The proposed topology has four arms comprised of series-connected half bridge modular multilevel converter cells. The rectangular pulse characteristics can be controlled via the software controller without any physical changes in power topology. The converter is capable of generating both bipolar and monopolar HV pulses with microsecond pulse durations at high a frequency rate with different characteristics. Hence, the proposed topology provides flexibility by software control, along with hardware modularity, scalability, and redundancy. Moreover, a cell's capacitance is relatively small, which drastically reduces the converter footprint. The adopted charging and discharging process of the cell capacitors in this topology eliminate the need of any voltage measurements or complex control for cell-capacitors voltage balance. Consequently, continuity of converter operation is assured under cell malfunction. In this paper, analysis and cell-capacitor sizing of the proposed topology are detailed. Converter operation is verified using MATLAB/Simulink simulation and scaled experimentation.

Differential-mode buck-boost inverters have merits such as reduced switch number, ability to provide voltages higher or lower than the input voltage magnitude, improved efficiency, reduced cost and size, and increased power density, especially in low-power applications. There are five buck-boost inverters that can provide flexible output voltage without the need of a large electrolytic input side capacitor, which degrades the reliability of inverters. The continuous input current of these inverters is appropriate for maximum power point tracking operation in photovoltaic and fuel cells applications. Three of the five inverters can be isolated with high-frequency-link transformers where the common-mode leakage current can be mitigated. However, the performance and control of such converters have not been discussed in detail. In this paper, the five possible single-stage three-phase differential-mode buck-boost inverters with continuous input current are investigated and compared in terms of total losses, maximum ripple current, total harmonic distortion, and device and passive element ratings. In addition, the possible methods are presented for eliminating the input third-order harmonic current, resulting from the stored energy in the passive elements, as well as the output second-order harmonic currents. The ability for isolating the input and output sides of the inverters with a small-high frequency transformers is discussed. A changeable-terminal 2.5-kW bidirectional inverter is used to validate the design flexibility of the inverter topologies, when digital signal processor-controlled.

The role of irreversible electroporation using pulsed electric field is to generate high-voltage (HV) pulses with a predefined magnitude and duration. These HV pulses are applied to the treatment chamber until decontamination of the sample is completed. In this paper, a new topology for HV rectangular pulse generation for water disinfection applications is introduced. The proposed topology has four arms comprised of series-connected half bridge modular multilevel converter cells. The rectangular pulse characteristics can be controlled via the software controller without any physical changes in power topology. The converter is capable of generating both bipolar and monopolar HV pulses with microsecond pulse durations at high a frequency rate with different characteristics. Hence, the proposed topology provides flexibility by software control, along with hardware modularity, scalability, and redundancy. Moreover, a cell's capacitance is relatively small, which drastically reduces the converter footprint. The adopted charging and discharging process of the cell capacitors in this topology eliminate the need of any voltage measurements or complex control for cell-capacitors voltage balance. Consequently, continuity of converter operation is assured under cell malfunction. In this paper, analysis and cell-capacitor sizing of the proposed topology are detailed. Converter operation is verified using MATLAB/Simulink simulation and scaled experimentation.

This paper presents a new three-phase inverter based on the Cuk converter. The main feature of the proposed topology is that the energy storage elements, such as inductors and capacitors, can be reduced in order to improve the reliability, and reduce size and total cost. The buck-boost inherent characteristic of the Cuk converter, depending on the time-varying duty ratio, provides flexibility for standalone and grid connected applications when the required output ac voltage is lower or greater than the dc side voltage. This property is not found in the conventional current source inverter when the dc input current is always greater than the ac output or in the conventional voltage source inverter as the output ac voltage is always lower than the dc input. The proposed system allows much smaller, more reliable nonelectrolytic capacitors to be used for energy source filtering. The new three-phase inverter is convenient for photovoltaic applications where continuous input currents are required for maximum power point tracking operation. Average large and small signal models are used to study the Cuk converter's nonlinear operation. The basic structure, control design, and MATLAB/SIMULINK results are presented. Practical results substantiate the design flexibility of the Cuk-based topology controlled by a TMSF280335 DSP.

This paper presents a new three-phase inverter based on the Cuk converter. The main feature of the proposed topology is that the energy storage elements, such as inductors and capacitors, can be reduced in order to improve the reliability, and reduce size and total cost. The buck-boost inherent characteristic of the Cuk converter, depending on the time-varying duty ratio, provides flexibility for standalone and grid connected applications when the required output ac voltage is lower or greater than the dc side voltage. This property is not found in the conventional current source inverter when the dc input current is always greater than the ac output or in the conventional voltage source inverter as the output ac voltage is always lower than the dc input. The proposed system allows much smaller, more reliable nonelectrolytic capacitors to be used for energy source filtering. The new three-phase inverter is convenient for photovoltaic applications where continuous input currents are required for maximum power point tracking operation. Average large and small signal models are used to study the Cuk converter's nonlinear operation. The basic structure, control design, and MATLAB/SIMULINK results are presented. Practical results substantiate the design flexibility of the Cuk-based topology controlled by a TMSF280335 DSP.